CCPA: Coordinated Cyber-Physical Attacks and Countermeasures in Smart Grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart grid, as one of the most critical infrastructures, is vulnerable to a wide variety of cyber and/or physical attacks. Recently, a new category of threats to smart grid, named coordinated cyber-physical attacks (CCPAs), are emerging. A key feature of CCPAs is to leverage cyber attacks to mask physical attacks which can cause power outages and potentially trigger cascading failures. In this paper, we investigate CCPAs in smart grid and show that an adversary can carefully synthesize a false data injection attack vector based on phasor measurement unit (PMU) measurements to neutralize the impact of physical attack vector, such that CCPAs could circumvent bad data detection without being detected. Specifically, we present two potential CCPAs, namely replay and optimized CCPAs, respectively, and analyze the adversary's required capability to construct them. Based on the analytical results, countermeasures are proposed to detect the two kinds of CCPAs, through known-secure PMU measurement verification (in the cyber space) and online tracking of the power system equivalent impedance (in the physical space), respectively. The implementation of CCPAs in smart grid and the effectiveness of countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it